Competitive Privacy: Secure Analysis on Integrated Sequence Data

Abstract

Sequence data analysis has been extensively studied in the literature. However, most previous work focuses on analyzing sequence data from a single source or party. In many applications such as logistics and network traffic analysis, sequence data comes from more than one source or party. When multiple autonomous organizations collaborate and integrate their sequence data to perform analysis, sensitive business information of individual parties can be easily leaked to the other parties. In this paper, we propose the notion of competitive privacy to model the privacy that should be protected when carrying out data analysis on integrated sequence data. We propose a query restriction algorithm that can reject malicious queries with low auditing overhead. Experimental results show that our proposed method guarantees the protection of competitive privacy with only a significantly small portion of queries being restricted.